Recommendation systems are increasingly becoming more prominent in improving online content discovery and enhancing user experience. The presentation of recommendations can be based, inter alia, on click through rate (CTR) estimation on a given recommendation. In many cases, recommendation systems are embedded within domains of dynamic nature where changes in properties of the domain may influence the performance of the recommender. For example, user interface elements within a webpage, independent of the recommender, may bias user attention and affect the probability for a click. One challenge of a recommendation system is to be able to detect these dynamic properties and perform user interaction analysis in order to avoid compromises in prediction quality.